Federated learning (FL) is a decentralized AI mechanism suitable for a large number of devices like in smart IoT. A major challenge of FL is the non-IID dataset problem, originating from the heterogeneous data collected by FL participants, leading to performance deterioration of the trained global model. There have been various attempts to rectify non-IID dataset, mostly focusing on manipulating the collected data. This paper, however, proposes a novel approach to ensure data IIDness by properly clustering and grouping mobile IoT nodes exploiting their geographical characteristics, so that each FL group can achieve IID dataset. We first provide an experimental evidence for the independence and identicalness features of IoT data according to the inter-device distance, and then propose Dynamic Clustering and Partial-Steady Grouping algorithms that partition FL participants to achieve near-IIDness in their dataset while considering device mobility. Our mechanism significantly outperforms benchmark grouping algorithms at least by 110 times in terms of the joint cost between the number of dropout devices and the evenness in per-group device count, with a mild increase in the number of groups only by up to 0.93 groups.

本文解决了联合学习中非独立同分布数据集的问题，主要是在参与者数据异构性导致训练全球模型性能下降。通过利用移动物联网节点的地理特征，本文提出了一种新颖的聚类与分组方法，确保每个联合学习组的数据集接近独立同分布。实验结果表明，我们的方法在设备中断数量和每组设备数量均衡性方面相较基准算法显著提高了性能。

地理节点聚类与分组以保证联合学习中的数据独立同分布性